Impact factors and publication times of original scientific research in radiology journals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: The time from article submission to publication in peer-reviewed scientific journals is variable and can be prolonged, which slows the dissemination of research and can influence the academic progress of authors. This study evaluated the publication times for articles in radiology journals, in particular the relationship between turnaround times and journal impact factors (IFs). METHODS: Bibliometric data was obtained from Journal Citation Reports to conduct a comparative analysis of radiology journals against those in other disciplines of clinical medicine using highest IF, median IF, cited half-life, immediacy index, and number of journals. Journals from various radiology subcategories were further examined to assess IF trends over time. The Pearson correlation coefficient was used to identify any statistically significant relationships between IF and other variables. RESULTS: Among 28 medical disciplines, there was a significant positive correlation of 0.63 between the number of journals and the highest journal IF of a given discipline. Among 135 radiology journals categorized into 12 subcategories, there was a similar significant correlation of 0.64. For high-ranking radiology journals, the median time from submission to publication online was 22.7 weeks [IQR = 9.3] and median time from submission to publication in print was 37.9 weeks [IQR = 7.1]. The former time interval showed a positive correlation of 0.58 with journal IF at p < 0.05. CONCLUSION: There is wide variation in the time from submission to publication in radiology journals. Authors can expect a longer turnaround time when publishing in higher-impact journals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.132 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.069 | 0.137 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it